论文标题
分析由Persyst可扩展HPC监控工具收集的性能属性
Analyzing Performance Properties Collected by the PerSyst Scalable HPC Monitoring Tool
论文作者
论文摘要
了解如何在大型HPC系统上执行科学应用的能力在分配HPC数据中心的资源方面非常重要。在本文中，我们描述了如何使用系统性能数据来识别：执行模式，可能的代码优化以及对系统监视的改进。我们还确定了使用机器学习技术来预测类似科学代码的性能的候选人。
The ability to understand how a scientific application is executed on a large HPC system is of great importance in allocating resources within the HPC data center. In this paper, we describe how we used system performance data to identify: execution patterns, possible code optimizations and improvements to the system monitoring. We also identify candidates for employing machine learning techniques to predict the performance of similar scientific codes.
